The value of coagulation index in thromboelastograph for predicting early pregnancy loss in IVF/ICSI cycles

The value of coagulation index in thromboelastograph for predicting early pregnancy loss in IVF/ICSI cycles | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The value of coagulation index in thromboelastograph for predicting early pregnancy loss in IVF/ICSI cycles Yanwei Zheng, Xiaohong Shi, Na Wang, Minfang Tao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8515894/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted 13 You are reading this latest preprint version Abstract Introduction : Early pregnancy loss (EPL) is common among couples undergoing assisted reproductive technology(ART) treatment. This study aimed to investigate whether thromboelastography parameters on the day of embryo transfer, either alone or in combination with other clinical parameters, could predict subsequent EPL in in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI) cycles. Methods : This study included 463 women who underwent IVF/ICSI cycles at the reproductive medicine center from May 2024 to May 2025. All these women underwent thromboelastography (TEG) on the day of embryo transfer, and their pregnancy outcomes were continuously followed up. For risk variables, we performed LASSO analysis. To analyze the risk factors associated with EPL, we employed univariate and multivariate logistic regression analyses. A nomogram was constructed for risk scoring and prediction. The area under the curve (AUC) was compared among different factors through the receiver operating characteristic (ROC) curve. Results : Among 463 women with clinical pregnancy, 129 (27.86%) experienced early pregnancy loss (< 12 weeks). There were significant differences in reaction time (R time), maximum amplitude (MA), expected percent lysis and coagulation index (CI) between women in the EPL group and those in the non-EPL group (p < 0.05). Multivariate logistic regression analysis showed that parity (OR = 1.614, 95%CI: 1.004–2.571), thyroid and endocrine disorders(OR = 2.202, 95%CI: 1.152–4.167), ovulatory dysfunction(OR = 4.408, 95%CI: 1.992–10.01) and CI (OR = 1.222, 95%CI: 1.077–1.397) were influencing factors for EPL in IVF/ICSI cycles. ROC curve analysis demonstrated that the optimal cutoff value for CI in predicting EPL is 0.75. The AUC for all five factors combined was 0.672, with a sensitivity of 71.3% and a specificity of 43.4%, which was better than any single factor. Conclusion : In IVF/ICSI cycles, a CI > 0.75(on the day of embryo transfer)was significantly associated with increased risk of EPL. Health sciences/Biomarkers Health sciences/Diseases Health sciences/Endocrinology Health sciences/Medical research Health sciences/Risk factors Coagulation index Early pregnancy loss Intracytoplasmic sperm injection In vitro fertilization Thromboelastography Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Early pregnancy loss (EPL) is defined as spontaneous abortion before 12 weeks' gestation. It includes biochemical pregnancy loss (elevated beta-hCG only followed by a decline) and clinical pregnancy loss (ultrasound confirmation of pregnancy followed by miscarriage). The prevalence of EPL in natural pregnancies is about 12.5%-18.7%, while in assisted reproductive technology (ART) cycles it can be as high as 20, with some studies reporting even higher rates[1,2,3,4]. According to the Global Burden of Disease (GBD) study, between 1990 and 2021, the global age-standardized prevalence rate (ASPR) of infertility increased by an average of 0.49% in men and 0.68% in women[5]. This represents a major public health challenge. The development of ART, such as in vitro fertilization (IVF), intracytoplasmic sperm injection (ICSI), and frozen-thawed embryo transfer (FET), have become critical approaches to infertility treatment.Significant progress has been made in infertility treatment in recent years, and clinical pregnancy rates with ART have increased significantly. Assisted reproductive technology (ART) has substantially expanded the possibility of pregnancy for populations with infertility, including those with tubal factor infertility, severe male factor infertility, or unexplained infertility who previously faced extremely low natural conception rates [6]. However, the higher prevalence of EPL in ART cycles still highlights its biological and technical shortcomings. The impact of pregnancy loss is not limited to physical risks such as infection, bleeding or potential complications, but also has significant psychological effects[7]. Some studies have shown that miscarriage can lead to damage to the uterine wall or lining, which in turn can be the cause of placental abnormalities in subsequent pregnancies[1,8]. Many infertility patients have high hopes for ART. EPL is undoubtedly the killer after the joy brought by ART, leaving patients physically, psychologically and financially burdened[9]. Given the dangers associated with EPL, an in-depth analysis of the factors that influence EPL and the optimisation of interventions are essential to increase the rate of live births on ART and to improve the prognosis of patients. The pathogenesis of EPL in ART involves a complex interaction between embryonic factors, maternal factors and medical factors[10,11].Traditionally, embryonic chromosomal abnormalities have been considered the main cause of EPL (about 60-70%)[12]. However, in recent years it has been found that the rate of post-implantation loss is still 15-20% for diploid embryos screened by preimplantation genetic testing (PGT-A)[13]. This is an indication that there are other non-genetic factors that contribute to the occurrence of EPL in ART. In recent years, there has been increasing evidence that acquired and inherited thrombotic tendencies (thrombophilia) are strongly associated with adverse pregnancy outcomes[14]. Physiologically, pregnancy itself is a hypercoagulable state. Currently, there is no consensus on the systematic assessment of coagulation in ART clinical practice: Some researchers recommend screening all patients with recurrent miscarriage or repeated implantation failure for thrombotic tendency. However, there are studies that take the opposite view[1,15,16,17,18]. In cases of thrombophilia, the hypercoagulable state can lead to the formation of microthrombi in the placental vasculature. In hereditary thrombophilia, due to genetic defects in anticoagulant proteins or abnormal function of coagulation factors, the normal balance between coagulation and anticoagulation is disrupted. In acquired thrombophilia like Antiphospholipid Syndrome (APS), Antiphospholipid Antibodies (aPLs) can bind to endothelial cells, platelets, and phospholipid - containing structures in the placenta. This binding activates the coagulation cascade, leading to the formation of thrombi. The resulting placental hypoxia can trigger apoptosis of trophoblast cells, disrupt the normal development of the placenta, and ultimately lead to EPL[17]. In this context, it is particularly important to investigate the relationship between coagulation assessment indices and EPL on ART. This will allow a better assessment of coagulation indices in relation to EPL in the ART population and optimise clinical strategies for ART. In clinical practice, conventional coagulation indices (e.g. prothrombin time (PT), activated partial thromboplastin time (APTT), fibrinogen (FIB) and D-dimer mainly reflect the dynamic balance of the body's coagulation-fibrinolytic system[19]. But TEG is a dynamic testing method that evaluates the entire coagulation process, comprehensively reflecting the stages from clot initiation, fibrin formation, platelet aggregation, to fibrinolysis[20,21]. The test includes seven key parameters: reaction time (R time), kinetic time (K time), alpha angle (α angle), maximum amplitude (MA), estimated percent lysis , coagulation index (CI), percentage of clot lysed within 30 minutes after MA(LY30 ). Therefore, this study aims to investigate the relationship between TEG parameters on embryo transfer day and the occurrence of EPL after embryo transfer in IVF/ICSI cycles, with the ultimate goal of optimizing ART clinical strategies, identifying high-risk EPL populations, and establishing theoretical foundations for preventive interventions. Materials and Methods This study was a prospective study of the data of participants who registered at the Department of Reproductive Medicine Center in Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine (Shanghai, China) from 2024 to 2025. The study was approved by the Ethics Committee of Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine (No.2018-R09). All methods were performed in accordance with the Declaration of Helsinki and relevant guidelines and regulations. All participants provided written informed consent. The way in which the data would be collected, used and protected was explained. To protect privacy, all participants were anonymised during the study and data were stored securely using encryption and access control techniques. Sample size：early pregnancy loss rate (P) ≈ 25% (0.25); allowable error (δ) = 5% (0.05); significance level (α) = 0.05 ; and power=0.8(two-tailed, Zα/2=1.96). the minimum required sample size was determined to be 288. A 10% buffer for potential data loss was added, resulting in a final sample size of 317. Participants and Data Collection The participant were undergoing assisted reproductive technology (ART) at the Centre for Reproductive Medicine. All participants underwent the necessary clinical laboratory tests and evaluations during IVF and embryo transfer (IVF-ET) procedures according to standard medical protocols. We performed TEG testing and collected the data on the day of IVF-ET. Additional information was obtained from the Hospital Information System(HIS), which mainly included sociodemographic and clinical information such as age, education level, husband's age, gravidity, parity, history of abortion, number of living children, body mass index(BMI), indications for ART, medical history, comorbidities, fertilization method. The ovarian stimulation protocol used was the gonadotropin-releasing hormone antagonist protocol (GnRH-ant protocol). All included cycles were frozen embryo transfer cycles. Embryo quality was graded using the Gardner scoring system and only Grade I/II embryos were included for transfer. All patients were followed from the day of embryo transfer until delivery. Thromboelastography (TEG) TEG is a bedside blood test that provides a comprehensive assessment of the entire clotting process in vitro (in a test cup). Unlike standard coagulation tests (e.g. PT, aPTT, platelet count) that analyze isolated parts of hemostasis, TEG measures the viscoelastic properties of a blood clot as it forms, strengthens, and eventually breaks down (fibrinolysis). The key parameters measured from this tracing are: R Time: The time from the start of the test until the first evidence of a clot forms (amplitude reaches 2mm). K Time: The time from the end of R until the clot reaches a fixed strength (amplitude of 20mm). It reflects the speed of clot formation. Alpha Angle (α-angle): The angle between the line in the middle of the tracing and the tangent to the developing curve. It also measures the rate of fibrin buildup and cross-linking. MA:The maximum strength/stiffness of the clot. LY30 (Lysis at 30 minutes):The percentage of clot dissolution 30 minutes after the MA is reached. Inclusion Criteria (1) The couple has reached the legal marriage age and obtained their marriage certificate (2) Have a clear indication for IVF/ICSI in accordance with medical norms and undergo embryo transfer at our center. (3) those patients with cleavage-stage embryo transfer (on day 5) (4) Conventional IVF and Intracytoplasmic Sperm Injection(ICSI) (5) Clinical pregnancy following IVF-ET was defined as the visualization of a gestational sac by ultrasound at 4-5 weeks post embryo transfer. (6) Singleton pregnancy Exclusion Criteria (1) Incomplete medical history information or missing TEG data (2) Chromosomal abnormalities or balanced translocation carriers in either spouse; (3) An ectopic pregnancy was diagnosed following in vitro fertilization and embryo transfer (IVF-ET) (4) Women with concomitant uterine malformations Statistical Analysis Statistical analyses were performed using SPSS 27.0, R software package (version 4.2.2). For all continuous variables, we first tested normality using the Shapiro-Wilk test (for sample size < 500) and Kolmogorov-Smirnov test (for sample size ≥ 500), in line with standard statistical practices. Mean ± standard deviation ( mean ± SD) was used for normally distributed continuous variables. Differences between two groups were analysed by independent samples t-test. Median (interquartile range) [Median(25th,75th percentiles)] was used for non-normal distribution of metric data, and Mann-Whitney U test for between-group differences. Count and categorical variables were expressed as number (percentage)[n(%)]. Statistical Tests for Categorical Variables: Chi-squared test was used for categorical variables when all expected frequencies ≥ 5, Fisher’s exact test was applied when any expected frequency < 5. Univariate logistic regression models were used to assess associations between early pregnancy loss and various influencing factors. LASSO regression (using the glmnet software package) and tenfold cross-validation were used for further selection.The tuning parameter λ in LASSO regression was determined by minimizing the mean squared error (MSE), ultimately retaining 6 predictor variables.The variables calculated from the LASSO regression were incorporated into a multivariate logistic regression using a backward approach to calculate risk factors and construct a multivariate regression model (using the glm {stats} software package). ROC curve analysis (R packages: pROC, ROCR,ggROC, and fbroc) was performed to evaluate the diagnostic value of predictive indicators for early pregnancy loss (<12 weeks). The optimal cut-off values were estimated by using the Youden index. A p-value <0.05 was considered statistically significant. Results Clinical characteristics of the participants We enrolled 974 eligible couples from an initial screening of 1,136 women and their spouses. Among 974 initially enrolled participants, 463 women with clinical pregnancies after embryo transfer, constituting the final study cohort. The screening process is shown in Figure 1. Of the 463 women, 129 (27.86%) experienced early pregnancy loss (<12 weeks). Of these 463 women, 129 (27.86%) experienced EPL (< 12 weeks). Of the 463 women with clinical pregnancies, 334 (72.14%) achieved successful delivery, resulting in a live birth rate of 34.29% among the total enrolled cohort (334/974). Comparative analysis revealed statistically significant differences between two groups (EPL group vs. non-EPL) in age, parity, number of children, ovulatory dysfunction, thyroid and endocrine disorders, CI, MA,R,EPL (all p <0.05, Table 1). Univariable logistic regression analysis of EPL in IVF-ET Following univariable logistic regression analysis, Table 2 shows that among the 26 variables included in the analysis, age, parity, ovulation disorders, thyroid and endocrine disorders, and TEG parameters (R, K,MA,CI, and angle) were significantly associated with early pregnancy loss in IVF-ET (all p <0.05). To further screen predictors of early pregnancy loss, we performed 10-fold cross-validated LASSO regression on 26 candidate variables. At the optimal penalty value (λ1se = 0.051204), the final model retained 6 influential factors (Figure 2-1, Figure 2-2). The selected variables were: maternal age, parity, comorbid thyroid and endocrine disorders, ovulatory dysfunction, R and CI. Multivariate logistic regression analysis nomogram pevelopment of early pregnancy loss in IVF-ET Backward stepwise regression was performed using likelihood ratio test with a removal threshold of p > 0.10. Multivariate logistic regression models were built (Table 3) with 5 variables. The final model demonstrated good calibration (Hosmer-Lemeshow test p = 0.516) and all variance inflation factors (VIF) were <1.1.The final five influences entered into the Multivariate regression equation: logit( P ( y =1)) = −2.89944 + 0.04785*Age + 0.47913*Parity + 0.78974*Thyroid and endocrine disorders + 1.48359*Ovulatory dysfunction + 2.0086*CI Assume that the variables for an individual: Age = 35, Parity = 2, Thyroid and endocrine disorders = 1(YES), Ovulatory dysfunction = 0(NO), CI =5, logit( P ( y =1)) = 1.52761. The probability of calculation is 0.822. The calculations are as follows: Multivariable logistic regression showed that ovulatory dysfunction (OR = 4.408 95%, CI: 1.992 – 10.01), thyroid and endocrine disorders (OR = 2.202, 95% CI: 1.152 – 4.167), parity(OR = 1.614, 95% CI: 1.004-2.571), and CI (OR = 1.222, 95% CI: 1.077 – 1.397) were risk factors for early pregnancy loss (all P < 0.05). Multivariable analysis showed that each additional 1-unit increase in parity was associated with a 61.4% increased risk of EPL. Pregnant women with thyroid and endocrine disorders had 2.202 times higher odds of early pregnancy loss compared to those without these conditions. Women with ovulatory dysfunction showed 4.408 times greater odds of pregnancy loss than those without ovulatory dysfunction. Each unit increase in CI was associated with a 22.2% elevated risk.The association with age did not reach statistical significance ( P = 0.059). We also constructed a nomogram (Figure 3) based on the results of the logistic regression analysis. The nomogram serves as a predictive tool for early pregnancy loss (EPL) in IVF/ICSI cycles, enabling risk assessment on the day of embryo transfer. Each predictive parameter is assigned a specific score, and the total score determines the probability of EPL occurrence. Receiver operating characteristic (ROC) curve analysis for early pregnancy loss in IVF-ET ROC curve analysis showed that CI in TEG predicted early pregnancy loss with a sensitivity of 61.2% and specificity of 56.9%. The optimal cut-off value for CI was determined to be 0.75, corresponding to the aforementioned sensitivity and specificity. The predictive value of other individual risk factors for EPL was inferior to that of CI (AUC = 0.592, as previously noted): maternal age (AUC=0.559), parity (AUC=0.551), thyroid and endocrine disorders (AUC=0.541), and ovulatory dysfunction (AUC=0.544). The AUC for the combination of all five factors was 0.672, with a sensitivity of 71.3% and specificity of 43.4% —a performance superior to that of any individual factor (Figure 4-2). Discussion In this study, we showed that CI on thromboelastography, maternal age, thyroid and endocrine disorders, Ovulatory dysfunction and parity were significantly associated with early pregnancy loss in IVF/ICSI cycles. In addition, the AUC value of CI in predicting EPL was superior to that of other risk factors. This study demonstrates that the integrated evaluation of all variables in our prediction model yielded optimal predictive performance (AUC=0.672). Most importantly, through a prospective investigation, we are the first to established the critical threshold for CI at 0.75. A CI value >0.75 was significantly associated with an increased risk of EPL in IVF/ICSI cycles (P=0.002). To the best of our knowledge, this represents the first well-defined predictive threshold for coagulation function in assisted reproduction, addressing the current gap in the study of quantitative standards for coagulation parameters associated with EPL. Previous studies primarily associated early pregnancy loss (EPL) with embryonic chromosomal abnormalities. However, subsequent research has demonstrated the multifactorial etiology of EPL, encompassing maternal age, endometrial tolerance, coagulation abnormalities, circulating abnormalities in circulating natural killer (NK) cells and other immune cells, low HCG levels, and obesity [10,22,23]. However, most existing studies lack specific quantitative indicators to assess the probability of EPL in IVF/ICSI cycles. In this study, we developed a predictive nomogram that integrates the CI on embryo transfer day to quantify the individualized risk of EPL occurrence. This nomogram aims to enable earlier intervention and improve live birth rates in IVF/ICSI cycles. Therefore, we can take necessary measures to prevent EPL as much as possible after confirmation of intrauterine pregnancy. For high-risk patients (CI > 0.75), we suggest more frequent follow-up and enhance comorbidity management. For patients with excessively high CI values, low-molecular-weight heparin (LMWH) may be used for prophylaxis when necessary. Studies have shown that LMWH can prevent thrombosis, and for pregnant women with a tendency to thrombosis, it can also improve placental blood flow[24, 25]. However, there is conflicting evidence: LMWH provides no significant benefit to patients without coagulation abnormalities and LMWH did not improve IVF pregnancy outcomes[26]. Evidence indicates that guilt, depression, loneliness and suicidal ideation are strongly associated with pregnancy loss[27,28]. A multicenter prospective cohort study conducted in London revealed that the prevalence of post-traumatic stress disorder (PTSD) among women after pregnancy loss were 34% at 1 month post-loss, 26% at 3 months post-loss, and 21% at 9 months post-loss, respectively The corresponding prevalence of moderate-to-severe depression were 10%, 8%, and 7% [29]. Most concerning is that these psychological traumas often present with few or no outward physical manifestations, making them likely to go unrecognized by healthcare professionals, family members and friends. Identifying risk factors for EPL and implementing targeted interventions is of significance that extends beyond ensuring a single successful pregnancy－it represents a crucial contribution to improving long-term maternal and neonatal health outcomes. A study of 954 IVF/ICSI cycles focusing on EPL showed that a low total antral follicle count (<10), estradiol/progesterone ratio < 1.1 and low serum hCG levels were significantly associated with EPL[30]. In our study, we found that EPL was significantly associated with CI. The odds of EPL were significantly increased (OR = 1.222, 95% CI: 1.077-1.397) in IVF/ICSI women when the CI on the day of embryo transfer was >0.75. Therefore, low-molecular-weight heparin(LMWH) may be used to prevent EPL in women with a CI >0.75 on the day of ET, following confirmation of intrauterine pregnancy and in the absence of other contraindications. A study of 575 women with recurrent miscarriage showed that LY30(a TEG parameter reflecting fibrinolysis) was associated with fetal loss [31]. Another study involving 160 patients with unexplained recurrent spontaneous abortion (URSA) showed that the R time, α-angle, and MA in TEG were independent risk factors for URSA [32]. TEG －derived CI is a comprehensive indicator reflecting overall coagulation status－higher CI values indicate a hypercoagulable state. For women undergoing IVF/ICSI, a hypercoagulable state on ET day may impair EPL outcomes through two key pathways: 1) uterine microcirculation disturbance: Hypercoagulability can reduce uterine spiral artery blood flow, limiting oxygen and nutrient delivery to the implanting embryo. This ischemia may disrupt embryo-maternal crosstalk, hinder placental formation, and ultimately leading to embryonic arrest. 2) increased thrombotic risk at the implantation site: A hypercoagulable microenvironment raises the risk of microthrombi formation in the decidual blood vessels; even subclinical microthrombosis can block the local blood supply to the gestational sac, preventing successful embryo implantation and maintenance [14, 18]. There are several limitations to the study. Firstly, the sample size was relatively modest. Secondly, our study did not include data on key hormone levels, which may also potentially affect EPL. Thirdly, futhermore, this study was a single-center study and lacks external validation. Finally, we did not conduct comparative TEG analyses across multiple time points. To address the aforementioned limitations, future research should compare TEG parameters at three key time points—baseline, hCG trigger day, and embryo transfer day—to elucidate the temporal dynamics of coagulation function in assisted reproductive technology－related EPL. In addition, we will expand the sample size and perform external validation in additional IVF/ICSI cohorts for further prospective investigations. This will help to optimize the predictive performance of the nomogram for EPL. Conclusion This study found that CI, thyroid and endocrine disorders, ovulatory dysfunction and parity were associated with EPL in women undergoing IVF/ICSI cycles. We further identified that a CI > 0.75 on the day of embryo transfer (ET) is significantly associated with an increased risk of EPL, with 0.75 established as the critical threshold. In addition, clinically. If the CI ＞ 0.75（on the day of ET), we suggest more frequent follow-up and intensified management of relevant comorbidities Declarations Ethics statement The study was approved by the Ethics Committee of Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine (No.2018-R09). This study was registered with the Chinese Clinical Trial Registry (ChiCTR2500101523). Author contributions ZY, WN and TM have drafted the articles or has made critical revisions to important parts of the content. ZY and TM are all corresponding authors. WN and SX were responsible for data analysis and the collection of the field data and for the follow-up work. ZY and TM designed and coordinated the study, reviewed and revised the final manuscript, and approved the final manuscript to go to publication. All authors made substantial contributions to this study. Acknowledgements We are very grateful to all the staff of Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine and the couples who participated in this study for their support and help. Conflict of interest The authors declare no conflict of interest. Funding NO Data Availability Some or all datasets generated during and/or analyzed during the current study have been made available to the journal, and can be obtained from the corresponding author and the journal upon reasonable request. References Quenby S, Gallos I D, Dhillon-Smith R K, et al. Miscarriage matters: the epidemiological, physical, psychological, and economic costs of early pregnancy loss. The Lancet. 2021;397(10285):1658-1667. Sara Neill. Management of early pregnancy loss. JAMA. 2023;329(16):1399-1400. Wang J X , Davies M J , Norman R J. Obesity increases the risk of spontaneous abortion during infertility treatment. Obes Res. 2012;10(6):551-554. Zdravka Veleva Aila Tiitinen Sirpa Vilska Christel Hydén-Granskog Candido Tomás Hannu Martikainen and Juha S. Tapanainen. High and low BMI increase the risk of miscarriage after IVF/ICSI and FET. Human Reproduction. 2008;23(4):878-884. Yu L, Ji H, QI Z,et al. Global, regional, and national prevalence and trends of infertility among individuals of reproductive age (15–49 years) from 1990 to 2021, with projections to 2040. Human Reproduction. 2025; 40(3):529–544 Carson S A , Kallen A N .Diagnosis and Management of Infertility: A Review[J].JAMA The Journal of the American Medical Association, 2021, 326(1):65-76. Mumusoglu S, Telek S B, Ata B. Preimplantation genetic testing for aneuploidy in unexplained recurrent pregnancy loss: a systematic review and meta-analysis.Fertility and Sterility. 2025;123(1):121-136. Chaiken S, Darney B, Schenck M,et al. Public Perceptions of Abortion Complications. Obstetric Anesthesia Digest. 2024;44(3):2. Tania Aziz, Samantha Gobioff, Rachel Flink-Bochacki. Effect of a family planning program on documented emotional support and reproductive goals counseling after previable pregnancy loss. Patient Educ Couns. 2022;105(10):3071-3077. Humaidan P, Ejdrup Bredkjær H, Bungum L,et al. GnRH agonist (buserelin) or hCG for ovulation induction in GnRH antagonist IVF/ICSI cycles: a prospective randomized study. Human Reproduction. 2005;20(11):3258-60. Fedorcsak P, Storeng R, Dale P O, et al. Obesity is a risk factor for early pregnancy loss after IVF or ICSI. Acta Obstetricia Et Gynecologica Scandinavica. 2000;79(1):43-48. Wang L, Jiang Y, Luo X,et al. Differential mRNA and lncRNA Expression Profiles Associated with Early Pregnancy Loss in ART Patients.Reproductive Sciences. 2025;32(1):229-237. Toni Jackson, Elyse Watkins. Early pregnancy loss. JAAPA,2021;34(3):22-27. Huang O, Ding H, Wu D, et al. A randomized, controlled clinical study of low-molecular-weight heparin improving pregnancy outcomes in PCOS women undergoing IVF: study protocol. Trials.2024;25(1):16. Lesley Regan, Rajendra Rai, Sotirios Saravelos, et al. Recurrent Miscarriage Green-top Guideline No. 17. BJOG. 2023;130(12):e9-e39. Dhillon-Smith R K, Melo P, Kaur N C A. Interventions to prevent miscarriage. Fertility and Sterility. 2023; 120(5):951-954. Niksa Vucić, Frleta M, Davor Petrović, et al. Thrombophilia, preeclampsia and other pregnancy complications. Acta Medica Croatica. 2009; 63(4):297-305. Malinowski A K. The Pathophysiology of Hypercoagulability and Infertility. Seminars in Reproductive Medicine. 2021;39(1-02):34-61. Offer Erez, Maha Othman, Anat Rabinovich, et al. DIC in Pregnancy - Pathophysiology, Clinical Characteristics, Diagnostic Scores, and Treatments. J Blood Med. 2022;(6)13:21-44. Xin Xie, Meng Wang, Yifan Lu, et al. Thromboelastography (TEG) in normal pregnancy and its diagnostic efficacy in patients with gestational hypertension, gestational diabetes mellitus, or preeclampsia. J Clin Lab Anal. 2021;35(2):e23623. Chiharu Suemitsu, Megumi Fudaba, Kohei Kitada, et al. Changes of Coagulation and Fibrinolytic Status Detected by Thromboelastography (TEG6s®) in Pregnancy, Labor, Early Postpartum, Postpartum Hemorrhage and Heparin Treatment for Perinatal Venous Thrombosis. Healthcare (Basel). 2022;10(10):2060. Pietro Bortoletto, Emma S Lucas, Pedro Melo, et al. Miscarriage syndrome: Linking early pregnancy loss to obstetric and age-related disorders. EBioMedicine. 2022.DOI: 10.1016/j.ebiom.2022.104134. Ferrell E L, Choudhry A A, Schon S B, et al. Obesity and In Vitro Fertilization. Seminars in reproductive medicine. 2023; 41(3/4):87-96. Akinshina S, Makatsariya A, Bitsadze V, Khizroeva J, Khamani N. Thromboprophylaxis in pregnant women with thrombophilia and a history of thrombosis. Journal of Perinatal Medicine. 2018;46:893-899 Borella F, Marozio L, Bertschy G, Botta G, Bertero L, Cassoni P, et al. Placenta-mediated pregnancy complications in women with a history of late fetal loss and placental infarction without thrombophilia: Risk of recurrence and efficacy of pharmacological prophylactic interventions. A 10-year retrospective study. The Journal of Maternal-Fetal & Neonatal Medicine. 2023;36 Sun B, Li L, Chen X, Sun Y. Effect of low-molecular-weight heparin in women undergoing frozen-thawed embryo transfer cycles: A retrospective cohort study. BMC Pregnancy and Childbirth. 2023;23 Donegan G, Noonan M, Bradshaw C,et al. Parents experiences of pregnancy following perinatal loss: An integrative review. Midwifery. 2023; DOI: 10.1016/j.midw.2023.103673. Kukulskien M , Emaitien N. Postnatal Depression and Post-Traumatic Stress Risk Following Miscarriage.International Journal of Environmental Research and Public Health. 2022;19(11):6515. Farren J, Jalmbrant M, Falconieri, et al. Differences in post-traumatic stress, anxiety and depression following miscarriage or ectopic pregnancy between women and their partners: multicenter prospective cohort study. Ultrasound in obstetrics & gynecology: the official journal of the International Society of Ultrasound in Obstetrics and Gynecology. 2021; 57(1):141-148. Liyan Wang, Lin Wang, Xia Yang,et al. Risk factors related to early pregnancy loss in fresh IVF/ICSI: An analysis of 954 embryo transfer cycles. Medicine (Baltimore). 2022;101(34):e30166. Mu F, Huo H, Wang C, et al. A new prognostic model for recurrent pregnancy loss: assessment of thyroid and thromboelastograph parameters. Frontiers in Endocrinology, 2024.1415786. Xu J, Yang Y, Guan G, Gao Y, Sun Q, Yuan G, et al. Prediction of unexplained recurrent miscarriages using thromboelastography. International Journal of Women's Health. 2024;16:1573-1581. Tables Table 1 Clinical characteristics and TEG of the participants Characteristics ALL non- Pregnancy loss Pregnancy loss p N=463 N=334 1 N=129 Age 32.00 [29.00,35.00] 31.00 [29.00,34.00] 32.00 [29.00,36.00] 0.047* Education level 0.620 Junior high school or below 100 (21.60%) 68 (20.36%) 32 (24.81%) High school 68 (14.69%) 48 (14.37%) 20 (15.50%) College 266 (57.45%) 198 (59.28%) 68 (52.71%) Postgraduate or above 29 (6.26%) 20 (5.99%) 9 (6.98%) Husband's age 33.00 [30.00,37.00] 33.00 [30.00,36.00] 34.00 [30.00,38.00] 0.157 Menarcheal age 13.00 [13.00,14.00] 13.00 [13.00,14.00] 13.00 [13.00,14.00] 0.467 BMI 22.06 [20.34,24.33] 21.93 [20.19,24.41] 22.66 [21.03,24.09] 0.087 Gravidity 0.756 0 248（53.56%） 179(53.59%) 69（53.49%） 1 115（24.84%） 87（26.05%） 28（21.71%） 2 54（11.67%） 38（11.38%） 16（12.40%） 3 26（5.62%） 17（5.09%） 9（6.98%） 4 20（4.32%） 13（3.90%） 7（5.43%） Parity 0.021* 0 386 (83.37%) 288 (86.23%) 98 (75.97%) 1 68 (14.69%) 40 (11.98%) 28 (21.71%) 2 9 (1.94%) 6 (1.80%) 3 (2.33%) Number of abortions 0.573 0 313 (67.60%) 224 (67.07%) 89 (68.99%) 1 97 (20.95%) 73 (21.86%) 24 (18.60%) 2 37 (7.99%) 24 (7.19%) 13 (10.08%) 3 16 (3.46%) 13 (3.89%) 3 (2.33%) Number of Spontaneous abortion 0.41 0 407 (87.90%) 290 (86.83%) 118 (90.77%) 1 46 (9.94%) 35 (10.48%) 11 (8.46%) 2 10 (2.16%) 9 (2.69%) 1 (0.77%) Number of children 0.038 * 0 387 (83.59%) 288 (86.23%) 99 (76.74%) 1 70 (15.12%) 42 (12.57%) 28 (21.71%) 2 6 (1.30%) 4 (1.20%) 2 (1.55%) Endometriosis 1.000 NO 447 (96.54%) 322 (96.41%) 125 (96.90%) YES 16 (3.46%) 12 (3.59%) 4 (3.10%) PCOS 0.148 NO 393 (84.88%) 278 (83.23%) 115 (89.15%) YES 70 (15.12%) 56 (16.77%) 14 (10.85%) Tubal factor 0.572 NO 140 (30.24%) 104 (31.14%) 36 (27.91%) YES 323 (69.76%) 230 (68.86%) 93 (72.09%) Ovulatory dysfunction 0.001 * NO 435 (93.95%) 322 (96.41%) 113 (87.60%) YES 28 (6.05%) 12 (3.59%) 16 (12.40%) Unexplained infertility 0.378 NO 448 (96.76%) 325 (97.31%) 123 (95.35%) YES 15 (3.24%) 9 (2.69%) 6 (4.65%) Male factor 0.524 NO 160 (34.56%) 112 (33.53%) 48 (37.21%) YES 303 (65.44%) 222 (66.47%) 81 (62.79%) Medical history &comorbidities Thyroid & endocrine disorders 0.015 * NO 415 (89.63%) 307 (91.92%) 108 (83.72%) YES 48 (10.37%) 27 (8.08%) 21 (16.28%) Recurrent pregnancy loss 0.53 NO 450 (97.19%) 323 (96.71%) 127 (98.45%) YES 13 (2.81%) 11 (3.29%) 2 (1.55%) Fertilization methods in ART 0.75 IVF 400 (86.39%) 287 (85.93%) 113 (87.60%) ICSI 63 (13.61%) 47 (14.07%) 16 (12.40%) TEG R 61.10 [56.10,65.15] 60.25 [55.90,64.68] 5.40 [4.80,6.40] 0.004 * MA 5.60 [4.90,6.50] 5.70 [5.00,6.50] 62.90 [57.40,66.20] 0.012 * LY30 1.40 [1.20,1.80] 1.50 [1.20,1.80] 0.10 [0.10,0.10] 0.054 K 0.10 [0.10,0.50] 0.10 [0.10,0.30] 1.40 [1.20,1.70] 0.123 estimated percent lysis 0.70 [-0.45,1.60] 0.50 [-0.58,1.50] 0.10 [0.10,0.90] 0.002 * CI 69.50 [65.20,72.50] 69.10 [64.82,72.30] 1.00 [-0.20,2.00] 0.030 * Angle 0.10 [0.10,0.10] 0.10 [0.10,0.10] 69.90 [66.80,73.40] 0.094 *: A p-value <0.05 was considered statistically significant Table 2 Univariable logistic regression analysis of the influencing factors for pregnancy loss in IVF/ICSI cycles. Characteristics B SE OR(95%CI) P Age 0.054 0.02359 1.056(1.008-1.106) 0.022 # Education level -0.111 0.11423 0.895(0.716-1.122) 0.332 Husband's age 0.028 0.01835 1.028(0.992-1.066) 0.129 Menarcheal age -0.078 0.0987 0.925(0.759-1.119) 0.427 Gravidity 0.075 0.09129 1.078(0.899-1.287) 0.410 Parity 0.551 0.22511 1.735(1.111-2.697) 0.014# Number of abortions -0.036 0.13344 0.965(0.737-1.246) 0.790 Number of Spontaneous abortion -0.397 0.28643 0.673(0.365-1.135) 0.166 Number of children 0.528 0.234 1.695（1.066-2.679） 0.024# BMI 0.043 0.03126 1.044(0.981-1.11) 0.170 Fertilization methods in ART -0.145 0.31002 0.865(0.459-1.557) 0.639 Recurrent pregnancy loss -0.771 0.77581 0.462(0.071-1.753) 0.320 Male factor -0.161 0.2159 0.851(0.559-1.305) 0.456 Unexplained infertility 0.566 0.53757 1.762(0.58-4.988) 0.292 Ovulatory dysfunction 1.335 0.39723 3.799(1.753-8.45) 0.001# Thyroid & endocrine disorders 0.793 0.31173 2.211(1.19-4.064) 0.011# Tubal factor 0.155 0.22911 1.168(0.75-1.845) 0.498 PCOS -0.504 0.31871 0.604(0.313-1.1) 0.114 Endometriosis -0.152 0.58689 0.859(0.237-2.518) 0.795 R -0.273 0.0939 0.761(0.63-0.911) 0.004# LY30 -0.13 0.08835 0.878(0.709-1.013) 0.141 K -0.309 0.17706 0.734(0.509-1.022) 0.080 estimated percent lysis -0.026 0.02312 0.974(0.927-1.016) 0.261 MA1 0.044 0.01768 1.045(1.01-1.082) 0.013# CI 0.194 0.06331 1.214(1.076-1.379) 0.002# Angle 0.033 0.01632 1.034(1.002-1.069) 0.041# #: A p-value <0.05 was considered statistically significant Table 3 Multivariable logistic regression analysis of the influencing factors for pregnancy loss IVF/ICSI cycles. characteristics B SE OR(95%CI) Z P Age 0.048 0.02533 1.049 (0.998-1.102) 1.889 0.059 Parity 0.479 0.23862 1.614 (1.004-2.571) 2.008 0.045 Thyroid and endocrine disorders 0.79 0.32618 2.202 (1.152-4.167) 2.421 0.015 Ovulatory dysfunction 1.484 0.40805 4.408 (1.992-10.01) 3.636 <0.001 CI 0.201 0.06608 1.222 (1.077-1.397) 3.04 0.002 Additional Declarations No competing interests reported. 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Shi","email":"","orcid":"","institution":"Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medici","correspondingAuthor":false,"prefix":"","firstName":"Xiaohong","middleName":"","lastName":"Shi","suffix":""},{"id":582542055,"identity":"3d139224-9fe4-4ae1-aa66-24c31624687b","order_by":2,"name":"Na Wang","email":"","orcid":"","institution":"Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medici","correspondingAuthor":false,"prefix":"","firstName":"Na","middleName":"","lastName":"Wang","suffix":""},{"id":582542056,"identity":"31f5c3db-a9b3-42a7-ae1c-86ea2cb6859f","order_by":3,"name":"Minfang Tao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxElEQVRIiWNgGAWjYLCCigoGZjCDhxjVYEVnzjAw85Cm5WwblEGUFnv2w0c3HJxXx24vkcD44G0bg7w5QVt40tJuHNx2mJlHIoHZcG4bg+HOBoIOyzG7/XHbAZAWNmneNoYEgwOEtPC/MbtxcE4dSAv7b+K0SOQAtTQwg21hJk7LjWdpNw4cA/rlzMNmyTnnJAw3ENLC3p987MaBmrpk9vbkgx/elNnIE7QFBpIZGBgbgLQEkeqBwI54paNgFIyCUTDiAAA2ejxsRdWGlQAAAABJRU5ErkJggg==","orcid":"","institution":"Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Minfang","middleName":"","lastName":"Tao","suffix":""}],"badges":[],"createdAt":"2026-01-05 01:23:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8515894/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8515894/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-026-43675-6","type":"published","date":"2026-03-28T16:09:23+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":101752199,"identity":"6535d22a-82d0-4491-a292-2f57d820c6a0","added_by":"auto","created_at":"2026-02-03 10:26:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":63914,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of study design\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8515894/v1/4ee98fe162b33f305c07d120.png"},{"id":101501138,"identity":"d3a3d60d-e7c7-4b45-8cfc-2a3f38bf93fd","added_by":"auto","created_at":"2026-01-30 13:28:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":98674,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA LASSO regression model for early pregnancy loss in IVF-ET\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8515894/v1/63437e81bbcde4dc7e0cca12.png"},{"id":101501139,"identity":"d3f47818-197f-4fea-8c60-6b3b863b5ba5","added_by":"auto","created_at":"2026-01-30 13:28:19","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":43589,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNomogram for the prediction of EPL in IVF/ICSI cycles.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe points of each factor were added to obtain the total points, and a vertical line was drawn on the total points to obtain the corresponding risk of EPL.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8515894/v1/ea204599fe6c3c6689986f37.png"},{"id":101501141,"identity":"2144a882-1555-4850-83b8-991b0c72a350","added_by":"auto","created_at":"2026-01-30 13:28:19","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":30123,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe ROC curves analysis for predicting of EPL in IVF-ET cycles\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 4-1: The AUC of CI\u003cstrong\u003e \u003c/strong\u003ein TEG predicted early pregnancy loss\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 4-2: Predictive performance (AUC) of the combined model incorporating CI, ovulatory dysfunction, thyroid disorders, and parity for EPL in IVF-ET cycles\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8515894/v1/5a9854caf236bcb946c00a52.png"},{"id":105754931,"identity":"48881c6e-5ec6-43a6-9867-18fb077a5c36","added_by":"auto","created_at":"2026-03-30 16:23:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1597991,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8515894/v1/feb1f7be-1f95-45d6-9391-84462682eea1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The value of coagulation index in thromboelastograph for predicting early pregnancy loss in IVF/ICSI cycles","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEarly pregnancy loss (EPL) is defined as spontaneous abortion before 12 weeks' gestation. It includes biochemical pregnancy loss (elevated beta-hCG only followed by a decline) and clinical pregnancy loss (ultrasound confirmation of pregnancy followed by miscarriage). The prevalence of EPL in natural pregnancies is about 12.5%-18.7%, while in assisted reproductive technology (ART) cycles it can be as high as 20, with some studies reporting even higher rates[1,2,3,4]. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAccording to the Global Burden of Disease (GBD) study, between 1990 and 2021, the global age-standardized prevalence rate (ASPR) of infertility increased by an average of 0.49% in men and 0.68% in women[5]. This represents a major public health challenge. The development of ART, such as in vitro fertilization (IVF), intracytoplasmic sperm injection (ICSI), and frozen-thawed embryo transfer (FET), have become critical approaches to infertility treatment.Significant progress has been made in infertility treatment in recent years, and clinical pregnancy rates with ART have increased significantly. Assisted reproductive technology (ART) has substantially expanded the possibility of pregnancy for populations with infertility, including those with tubal factor infertility, severe male factor infertility, or unexplained infertility who previously faced extremely low natural conception rates [6]. However, the higher prevalence of EPL in ART cycles still highlights its biological and technical shortcomings. The impact of pregnancy loss is not limited to physical risks such as infection, bleeding or potential complications, but also has significant psychological effects[7]. Some studies have shown that miscarriage can lead to damage to the uterine wall or lining, which in turn can be the cause of placental abnormalities in subsequent pregnancies[1,8]. Many infertility patients have high hopes for ART. EPL is undoubtedly the killer after the joy brought by ART, leaving patients physically, psychologically and financially burdened[9]. Given the dangers associated with EPL, an in-depth analysis of the factors that influence EPL and the optimisation of interventions are essential to increase the rate of live births on ART and to improve the prognosis of patients.\u003c/p\u003e\n\u003cp\u003eThe pathogenesis of EPL in ART involves a complex interaction between embryonic factors, maternal factors and medical factors[10,11].Traditionally, embryonic chromosomal abnormalities have been considered the main cause of EPL (about 60-70%)[12]. However, in recent years it has been found that the rate of post-implantation loss is still 15-20% for diploid embryos screened by preimplantation genetic testing (PGT-A)[13]. This is an indication that there are other non-genetic factors that contribute to the occurrence of EPL in ART. In recent years, there has been increasing evidence that acquired and inherited thrombotic tendencies (thrombophilia) are strongly associated with adverse pregnancy outcomes[14]. Physiologically, pregnancy itself is a hypercoagulable state. Currently, there is no consensus on the systematic assessment of coagulation in ART clinical practice: Some researchers recommend screening all patients with recurrent miscarriage or repeated implantation failure for thrombotic tendency. However, there are studies that take the opposite view[1,15,16,17,18].\u003c/p\u003e\n\u003cp\u003eIn cases of thrombophilia, the hypercoagulable state can lead to the formation of microthrombi in the placental vasculature. In hereditary thrombophilia, due to genetic defects in anticoagulant proteins or abnormal function of coagulation factors, the normal balance between coagulation and anticoagulation is disrupted. In acquired thrombophilia like Antiphospholipid Syndrome (APS), Antiphospholipid Antibodies (aPLs) can bind to endothelial cells, platelets, and phospholipid - containing structures in the placenta. This binding activates the coagulation cascade, leading to the formation of thrombi. The resulting placental hypoxia can trigger apoptosis of trophoblast cells, disrupt the normal development of the placenta, and ultimately lead to EPL[17].\u003c/p\u003e\n\u003cp\u003eIn this context, it is particularly important to investigate the relationship between coagulation assessment indices and EPL on ART. This will allow a better assessment of coagulation indices in relation to EPL in the ART population and optimise clinical strategies for ART. In clinical practice, conventional coagulation indices (e.g. prothrombin time (PT), activated partial thromboplastin time (APTT), fibrinogen (FIB) and D-dimer mainly reflect the dynamic balance of the body's coagulation-fibrinolytic system[19]. But TEG is a dynamic testing method that evaluates the entire coagulation process, comprehensively reflecting the stages from clot initiation, fibrin formation, platelet aggregation, to fibrinolysis[20,21]. The test includes seven key parameters: reaction time (R time), kinetic time (K time),\u0026nbsp;alpha angle (α angle), maximum amplitude (MA), estimated percent lysis , coagulation index (CI), percentage of clot lysed within 30 minutes after MA(LY30\u0026nbsp;).\u003c/p\u003e\n\u003cp\u003eTherefore, this study aims to investigate the relationship between TEG parameters on embryo transfer day and the occurrence of EPL after embryo transfer in IVF/ICSI cycles, with the ultimate goal of optimizing ART clinical strategies, identifying high-risk EPL populations, and establishing theoretical foundations for preventive interventions.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eThis study was a prospective study of the data of participants who registered at the Department of Reproductive Medicine Center in Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine (Shanghai, China) from 2024 to 2025. The study was approved by the Ethics Committee of Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine (No.2018-R09). All methods were performed in accordance with the Declaration of Helsinki and relevant guidelines and regulations. All participants provided written informed consent. The way in which the data would be collected, used and protected was explained. To protect privacy, all participants were anonymised during the study and data were stored securely using encryption and access control techniques. Sample size：early pregnancy loss rate (P)\u0026nbsp;≈\u0026nbsp;25% (0.25); allowable error (δ) = 5% (0.05); significance level (α) = 0.05 ; and power=0.8(two-tailed, Zα/2=1.96). the minimum required sample size was determined to be 288. A 10% buffer for potential data loss was added, resulting in a final sample size of 317.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eParticipants and Data Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe participant were undergoing assisted reproductive technology (ART) at the Centre for Reproductive Medicine. All participants underwent the necessary clinical laboratory tests and evaluations during IVF and embryo transfer (IVF-ET) procedures according to standard medical protocols. We performed TEG testing and collected the data on the day of IVF-ET. Additional information was obtained from the Hospital Information System(HIS), which mainly included sociodemographic and clinical information such as age, education level, husband's age, gravidity, parity, history of abortion, number of living children, body mass index(BMI), indications for ART, medical history, comorbidities, fertilization method. The ovarian stimulation protocol used was the gonadotropin-releasing hormone antagonist protocol (GnRH-ant protocol). All included cycles were frozen \u0026nbsp;embryo transfer cycles.\u0026nbsp;Embryo quality was graded using the Gardner scoring system and only Grade I/II embryos were included for transfer.\u0026nbsp;All patients were followed from\u0026nbsp;the day of embryo transfer until delivery.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThromboelastography (TEG)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTEG is a bedside blood test that provides a comprehensive assessment of the entire clotting process in vitro (in a test cup). Unlike standard coagulation tests (e.g. PT, aPTT, platelet count) that analyze isolated parts of hemostasis, TEG measures the viscoelastic properties of a blood clot as it forms, strengthens, and eventually breaks down (fibrinolysis). The key parameters measured from this tracing are:\u003c/p\u003e\n\u003cp\u003eR Time: The time from the start of the test until the first evidence of a clot forms (amplitude reaches 2mm).\u003c/p\u003e\n\u003cp\u003eK Time: The time from the end of R until the clot reaches a fixed strength (amplitude of 20mm). It reflects the speed of clot formation.\u003c/p\u003e\n\u003cp\u003eAlpha Angle (α-angle): The angle between the line in the middle of the tracing and the tangent to the developing curve. It also measures the rate of fibrin buildup and cross-linking.\u003c/p\u003e\n\u003cp\u003eMA:The maximum strength/stiffness of the clot.\u003c/p\u003e\n\u003cp\u003eLY30 (Lysis at 30 minutes):The percentage of clot dissolution 30 minutes after the MA is reached.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInclusion Criteria\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;(1) The couple has reached the legal marriage age and obtained their marriage certificate\u003c/p\u003e\n\u003cp\u003e(2) Have a clear indication for IVF/ICSI in accordance with medical norms and undergo embryo transfer at our center.\u003c/p\u003e\n\u003cp\u003e(3) those patients with cleavage-stage embryo transfer (on day 5)\u003c/p\u003e\n\u003cp\u003e(4) Conventional IVF and Intracytoplasmic Sperm Injection(ICSI)\u003c/p\u003e\n\u003cp\u003e(5) Clinical pregnancy following IVF-ET was defined as the visualization of a gestational sac by ultrasound at 4-5 weeks post embryo transfer.\u003c/p\u003e\n\u003cp\u003e(6) Singleton pregnancy\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExclusion Criteria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(1) Incomplete medical history information or missing TEG data\u003cbr\u003e\u0026nbsp;(2) Chromosomal abnormalities or balanced translocation carriers in either spouse;\u003cbr\u003e\u0026nbsp;(3) An ectopic pregnancy was diagnosed following in vitro fertilization and embryo transfer (IVF-ET)\u003c/p\u003e\n\u003cp\u003e(4) Women with concomitant uterine malformations\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analyses were performed using SPSS 27.0, R software package (version 4.2.2). For all continuous variables, we first tested normality using the Shapiro-Wilk test (for sample size \u0026lt; 500) and Kolmogorov-Smirnov test (for sample size ≥ 500), in line with standard statistical practices. Mean ± standard deviation ( mean ± SD) was used for normally distributed continuous variables. Differences between two groups were analysed by independent samples t-test. Median (interquartile range) [Median(25th,75th percentiles)] was used for non-normal distribution of metric data, and Mann-Whitney U test for between-group differences. Count and categorical variables were expressed as number (percentage)[n(%)]. Statistical Tests for Categorical Variables: Chi-squared test was used for categorical variables when all expected frequencies ≥ 5, Fisher’s exact test was applied when any expected frequency \u0026lt; 5. Univariate logistic regression models were used to assess associations between early pregnancy loss and various influencing factors. LASSO regression (using the glmnet software package) and tenfold cross-validation were used for further selection.The tuning parameter λ in LASSO regression was determined by minimizing the mean squared error (MSE), ultimately retaining 6 predictor variables.The variables calculated from the LASSO regression were incorporated into a multivariate logistic regression using a backward approach to calculate risk factors and construct a multivariate regression model (using the glm {stats} software package). ROC curve analysis (R packages: pROC, ROCR,ggROC, and fbroc) was performed to evaluate the diagnostic value of predictive indicators for early pregnancy loss (\u0026lt;12 weeks). The optimal cut-off values were estimated by using the Youden index. A p-value \u0026lt;0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eClinical characteristics of the participants\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe enrolled 974 eligible couples from an initial screening of 1,136 women and their spouses. Among 974 initially enrolled participants, 463 women with clinical pregnancies after embryo transfer, constituting the final study cohort. The screening process is shown in Figure 1. Of the 463 women, 129 (27.86%) experienced early pregnancy loss (\u0026lt;12 weeks). Of these 463 women, 129 (27.86%) experienced EPL (\u0026lt; 12 weeks). Of the 463 women with clinical pregnancies, 334 (72.14%) achieved successful delivery, resulting in a live birth rate of 34.29% among the total enrolled cohort (334/974). Comparative analysis revealed statistically significant differences between two groups (EPL group vs. non-EPL) in age, parity, number of children, ovulatory dysfunction, thyroid and endocrine disorders, CI, MA,R,EPL (all p \u0026lt;0.05, Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUnivariable logistic regression analysis of EPL in IVF-ET\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFollowing univariable logistic regression analysis, Table 2 shows that among the 26 \u0026nbsp;variables included in the analysis, age, parity, ovulation disorders, thyroid and endocrine \u0026nbsp;disorders, and TEG parameters (R, K,MA,CI, and angle) were significantly associated with early pregnancy loss in IVF-ET (all p \u0026lt;0.05). To further screen predictors of early pregnancy loss, we performed 10-fold cross-validated LASSO regression on 26 candidate variables. At the optimal penalty value (\u0026lambda;1se = 0.051204), the final model retained 6 influential factors (Figure 2-1, Figure 2-2). The selected variables were: maternal age, parity, comorbid thyroid and endocrine disorders, ovulatory dysfunction, R and CI. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultivariate logistic regression analysis nomogram pevelopment of early pregnancy loss in IVF-ET\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBackward stepwise regression was performed using likelihood ratio test with a removal threshold of p \u0026gt; 0.10. Multivariate logistic regression models were built (Table 3) with 5 variables. The final model demonstrated good calibration (Hosmer-Lemeshow test p = 0.516) and all variance inflation factors (VIF) were \u0026lt;1.1.The final five influences entered into the Multivariate regression equation:\u003c/p\u003e\n\u003cp\u003elogit(\u003cem\u003eP\u003c/em\u003e(\u003cem\u003ey\u003c/em\u003e=1))\u0026nbsp;= \u0026minus;2.89944 + 0.04785*Age + 0.47913*Parity + 0.78974*Thyroid and endocrine disorders + 1.48359*Ovulatory dysfunction + 2.0086*CI\u003c/p\u003e\n\u003cp\u003eAssume that the variables for an individual: Age = 35, Parity = 2, Thyroid and endocrine disorders = 1(YES), Ovulatory dysfunction = 0(NO), CI =5, logit(\u003cem\u003eP\u003c/em\u003e(\u003cem\u003ey\u003c/em\u003e=1)) = 1.52761. The probability of calculation is 0.822. The calculations are as follows:\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cp\u003eMultivariable logistic regression showed that ovulatory dysfunction (OR = 4.408 95%, CI: 1.992 \u0026ndash; 10.01), thyroid and endocrine disorders (OR = 2.202, 95% CI: 1.152 \u0026ndash; 4.167), parity(OR = 1.614, 95% CI: 1.004-2.571), and CI (OR = 1.222, 95% CI: 1.077 \u0026ndash; 1.397) were risk factors for early pregnancy loss (all \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). Multivariable analysis showed that each additional 1-unit increase in parity was associated with a 61.4% increased risk of EPL. Pregnant women with thyroid and endocrine disorders had 2.202 times higher odds of early pregnancy loss compared to those without these conditions. Women with ovulatory dysfunction showed 4.408 times greater odds of pregnancy loss than those without ovulatory dysfunction. Each unit increase in CI was associated with a 22.2% elevated risk.The association with age did not reach statistical significance (\u003cem\u003eP\u003c/em\u003e = 0.059).\u003c/p\u003e\n\u003cp\u003eWe also constructed a nomogram (Figure 3) based on the results of the logistic regression analysis. The nomogram serves as a predictive tool for early pregnancy loss (EPL) in IVF/ICSI cycles, enabling risk assessment on the day of embryo transfer. Each predictive parameter is assigned a specific score, and the total score determines the probability of EPL occurrence.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eReceiver operating characteristic (ROC) curve analysis for early pregnancy loss in IVF-ET\u003c/p\u003e\n\u003cp\u003eROC curve analysis showed that CI in TEG predicted early pregnancy loss with a sensitivity of 61.2% and specificity of 56.9%. The optimal cut-off value for CI was determined to be 0.75, corresponding to the aforementioned sensitivity and specificity. The predictive value of other individual risk factors for EPL was inferior to that of \u0026nbsp;CI (AUC = 0.592, as previously noted): maternal age (AUC=0.559), parity (AUC=0.551), thyroid and endocrine disorders (AUC=0.541), and ovulatory dysfunction (AUC=0.544). The AUC for the combination of all five factors was 0.672, \u0026nbsp; with a sensitivity of 71.3% and specificity of 43.4% \u0026mdash;a performance superior to that of any individual factor (Figure 4-2).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we showed that CI on thromboelastography, maternal age, thyroid and endocrine disorders, Ovulatory dysfunction and parity were significantly associated with early pregnancy loss in IVF/ICSI cycles. In addition, the AUC value of CI in predicting EPL was superior to that of other risk factors. This study demonstrates that the integrated evaluation of all variables in our prediction model yielded optimal predictive performance (AUC=0.672). Most importantly, through a prospective investigation, we are the first to established the critical threshold for CI at 0.75. A CI value \u0026gt;0.75 was significantly associated with an increased risk of EPL in IVF/ICSI cycles (P=0.002). To the best of our knowledge, this represents the first well-defined predictive threshold for coagulation function in assisted reproduction, addressing the current gap in the study of quantitative standards for coagulation parameters associated with EPL.\u003c/p\u003e\n\u003cp\u003ePrevious studies primarily associated early pregnancy loss (EPL) with embryonic chromosomal abnormalities. However, subsequent research has demonstrated the multifactorial etiology of EPL, encompassing maternal age, endometrial tolerance, coagulation abnormalities, circulating abnormalities in circulating natural killer (NK) \u0026nbsp;cells and other immune cells, low HCG levels, and obesity [10,22,23]. However, most existing studies lack specific quantitative indicators to assess the probability of EPL in IVF/ICSI cycles. In this study, we developed a predictive nomogram that integrates the CI on embryo transfer day to quantify the individualized risk of EPL occurrence. This nomogram aims to enable earlier intervention and improve live birth rates in IVF/ICSI cycles. Therefore, we can take necessary measures to prevent EPL as much as possible after confirmation of intrauterine pregnancy. For high-risk patients (CI \u0026gt; 0.75), we suggest more frequent follow-up and enhance comorbidity management. For patients with excessively high CI values, low-molecular-weight heparin (LMWH) may be used for prophylaxis when necessary. Studies have shown that LMWH can prevent thrombosis, and for pregnant women with a tendency to thrombosis, it can also improve placental blood flow[24, 25]. However, there is conflicting evidence: LMWH provides no significant benefit to patients without coagulation abnormalities and LMWH did not improve IVF pregnancy outcomes[26].\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Evidence indicates that guilt, depression, loneliness and suicidal ideation are strongly associated with pregnancy loss[27,28]. A multicenter prospective cohort study conducted in London revealed that the prevalence of post-traumatic stress disorder (PTSD) among women after pregnancy loss were 34% at 1 month post-loss, 26% at 3 months post-loss, and 21% at 9 months post-loss, respectively The corresponding prevalence of moderate-to-severe depression were 10%, 8%, and 7% [29]. Most concerning is that these psychological traumas often present with few or no outward physical manifestations, making them likely to go unrecognized by healthcare professionals, family members and friends. Identifying risk factors for EPL and implementing targeted interventions is of significance that extends beyond ensuring a single successful pregnancy－it represents a crucial contribution to improving long-term maternal and neonatal health outcomes. A study of 954 IVF/ICSI cycles focusing on EPL showed that a low total antral follicle count (\u0026lt;10), estradiol/progesterone ratio \u0026lt; 1.1 and low serum hCG levels were significantly associated with EPL[30]. In our study, we found that EPL was significantly associated with CI. The odds of EPL were significantly increased (OR = 1.222, 95% CI: 1.077-1.397) in IVF/ICSI women when the CI on the day of embryo transfer was \u0026gt;0.75.\u0026nbsp;Therefore, low-molecular-weight heparin(LMWH) may be used to prevent EPL in women with a CI \u0026gt;0.75 on the day of ET, following confirmation of intrauterine pregnancy and in the absence of other contraindications.\u003c/p\u003e\n\u003cp\u003eA study of 575 women\u0026nbsp;with recurrent miscarriage showed\u0026nbsp;that LY30(a TEG parameter reflecting fibrinolysis)\u0026nbsp;was associated with fetal loss [31]. Another study involving 160 patients with unexplained recurrent spontaneous abortion (URSA) showed that the R time,\u0026nbsp;\u0026alpha;-angle, and MA in TEG were independent risk factors for URSA [32]. TEG\u0026nbsp;－derived CI is a comprehensive indicator reflecting overall coagulation status－higher CI values indicate a hypercoagulable state. For women undergoing IVF/ICSI, a hypercoagulable state on ET day may impair EPL outcomes through two key pathways: 1) uterine microcirculation disturbance: Hypercoagulability can reduce uterine spiral artery blood flow, limiting oxygen and nutrient delivery to the implanting embryo. This ischemia may disrupt embryo-maternal crosstalk, hinder placental formation, and ultimately leading to embryonic arrest. 2) increased thrombotic risk at the implantation site: A hypercoagulable microenvironment raises the risk of microthrombi formation in the decidual blood vessels; even subclinical microthrombosis can block the local blood supply to the gestational sac, preventing successful embryo implantation and maintenance [14, 18].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThere are several limitations to the study. Firstly, the sample size was relatively modest. Secondly, our study did not include data on key hormone levels, which may also potentially affect EPL. Thirdly, futhermore, this study was a single-center study and lacks external validation. Finally, we did not conduct comparative TEG analyses across multiple time points. To address the aforementioned limitations, future research should compare TEG parameters at three key time points\u0026mdash;baseline, hCG trigger day, and embryo transfer day\u0026mdash;to elucidate the temporal dynamics of coagulation function in assisted reproductive technology－related EPL. In addition, we will expand the sample size and perform external validation in additional IVF/ICSI cohorts for further prospective investigations. This will help to optimize the predictive performance of the nomogram for EPL.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study found that CI, thyroid and endocrine disorders, ovulatory dysfunction and parity were associated with EPL in women undergoing IVF/ICSI cycles. We further identified that a CI \u0026gt; 0.75 on the day of embryo transfer (ET) is significantly associated with an increased risk of EPL, with 0.75 established as the critical threshold. In addition, clinically. If the CI ＞ 0.75（on the day of ET), we suggest more frequent follow-up and intensified management of relevant comorbidities\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The study was approved by the Ethics Committee of Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine (No.2018-R09). This study was registered with the Chinese Clinical Trial Registry (ChiCTR2500101523).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZY, WN and TM have drafted the articles or has made critical revisions to important parts of the content. ZY and TM are all corresponding authors. WN and SX were responsible for data analysis and the collection of the field data and for the follow-up work. ZY and TM designed and coordinated the study, reviewed and revised the final manuscript, and approved the final manuscript to go to publication. All authors made substantial contributions to this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are very grateful to all the staff of Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine and the couples who participated in this study for their support and help.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNO\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSome or all datasets generated during and/or analyzed during the current study have been made available to the journal, and can be obtained from the corresponding author and the journal upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eQuenby S, Gallos I D, Dhillon-Smith R K, et al. Miscarriage matters: the epidemiological, physical, psychological, and economic costs of early pregnancy loss. The Lancet. 2021;397(10285):1658-1667.\u003c/li\u003e\n \u003cli\u003eSara Neill. Management of early pregnancy loss. JAMA. 2023;329(16):1399-1400.\u003c/li\u003e\n \u003cli\u003eWang J X , Davies M J , Norman R J. Obesity increases the risk of spontaneous abortion during infertility treatment. Obes Res. 2012;10(6):551-554.\u003c/li\u003e\n \u003cli\u003eZdravka Veleva Aila Tiitinen Sirpa Vilska Christel Hyd\u0026eacute;n-Granskog Candido Tom\u0026aacute;s Hannu Martikainen and Juha S. Tapanainen. High and low BMI increase the risk of miscarriage after IVF/ICSI and FET. Human Reproduction. 2008;23(4):878-884.\u003c/li\u003e\n \u003cli\u003eYu L, Ji H, QI Z,et al. Global, regional, and national prevalence and trends of infertility among individuals of reproductive age (15\u0026ndash;49 years) from 1990 to 2021, with projections to 2040. Human Reproduction. 2025; 40(3):529\u0026ndash;544\u003c/li\u003e\n \u003cli\u003eCarson S A , Kallen A N .Diagnosis and Management of Infertility: A Review[J].JAMA The Journal of the American Medical Association, 2021, 326(1):65-76.\u003c/li\u003e\n \u003cli\u003eMumusoglu S, Telek S B, Ata B. Preimplantation genetic testing for aneuploidy in unexplained recurrent pregnancy loss: a systematic review and meta-analysis.Fertility and Sterility. 2025;123(1):121-136.\u003c/li\u003e\n \u003cli\u003eChaiken S, Darney B, Schenck M,et al. Public Perceptions of Abortion Complications. Obstetric Anesthesia Digest. 2024;44(3):2.\u003c/li\u003e\n \u003cli\u003eTania Aziz, Samantha Gobioff, Rachel Flink-Bochacki. Effect of a family planning program on documented emotional support and reproductive goals counseling after previable pregnancy loss. Patient Educ Couns. 2022;105(10):3071-3077.\u003c/li\u003e\n \u003cli\u003eHumaidan P, Ejdrup Bredkj\u0026aelig;r H, Bungum L,et al. GnRH agonist (buserelin) or hCG for ovulation induction in GnRH antagonist IVF/ICSI cycles: a prospective randomized study. Human Reproduction. 2005;20(11):3258-60.\u003c/li\u003e\n \u003cli\u003eFedorcsak P, Storeng R, Dale P O, et al. Obesity is a risk factor for early pregnancy loss after IVF or ICSI. Acta Obstetricia Et Gynecologica Scandinavica. 2000;79(1):43-48.\u003c/li\u003e\n \u003cli\u003eWang L, Jiang Y, Luo X,et al. Differential mRNA and lncRNA Expression Profiles Associated with Early Pregnancy Loss in ART Patients.Reproductive Sciences. 2025;32(1):229-237.\u003c/li\u003e\n \u003cli\u003eToni Jackson, Elyse Watkins. Early pregnancy loss. JAAPA,2021;34(3):22-27.\u003c/li\u003e\n \u003cli\u003eHuang O, Ding H, Wu D, et al. A randomized, controlled clinical study of low-molecular-weight heparin improving pregnancy outcomes in PCOS women undergoing IVF: study protocol. Trials.2024;25(1):16.\u003c/li\u003e\n \u003cli\u003eLesley Regan, Rajendra Rai, Sotirios Saravelos, et al. Recurrent Miscarriage Green-top Guideline No. 17. BJOG. 2023;130(12):e9-e39.\u003c/li\u003e\n \u003cli\u003eDhillon-Smith R K, Melo P, Kaur N C A. Interventions to prevent miscarriage. Fertility and Sterility. 2023; 120(5):951-954.\u003c/li\u003e\n \u003cli\u003eNiksa Vucić, Frleta M, Davor Petrović, et al. Thrombophilia, preeclampsia and other pregnancy complications. Acta Medica Croatica. 2009; 63(4):297-305.\u003c/li\u003e\n \u003cli\u003eMalinowski A K. The Pathophysiology of Hypercoagulability and Infertility. Seminars in Reproductive Medicine. 2021;39(1-02):34-61.\u003c/li\u003e\n \u003cli\u003eOffer Erez, Maha Othman, Anat Rabinovich, et al. DIC in Pregnancy - Pathophysiology, Clinical Characteristics, Diagnostic Scores, and Treatments. J Blood Med. 2022;(6)13:21-44.\u003c/li\u003e\n \u003cli\u003eXin Xie, Meng Wang, Yifan Lu, et al. Thromboelastography (TEG) in normal pregnancy and its diagnostic efficacy in patients with gestational hypertension, gestational diabetes mellitus, or preeclampsia. J Clin Lab Anal. 2021;35(2):e23623.\u003c/li\u003e\n \u003cli\u003eChiharu Suemitsu, Megumi Fudaba, Kohei Kitada, et al. Changes of Coagulation and Fibrinolytic Status Detected by Thromboelastography (TEG6s\u0026reg;) in Pregnancy, Labor, Early Postpartum, Postpartum Hemorrhage and Heparin Treatment for Perinatal Venous Thrombosis. Healthcare (Basel). 2022;10(10):2060.\u003c/li\u003e\n \u003cli\u003ePietro Bortoletto, Emma S Lucas, Pedro Melo, et al. Miscarriage syndrome: Linking early pregnancy loss to obstetric and age-related disorders. EBioMedicine. 2022.DOI: 10.1016/j.ebiom.2022.104134.\u003c/li\u003e\n \u003cli\u003eFerrell E L, Choudhry A A, Schon S B, et al. Obesity and In Vitro Fertilization. Seminars in reproductive medicine. 2023; 41(3/4):87-96.\u003c/li\u003e\n \u003cli\u003eAkinshina S, Makatsariya A, Bitsadze V, Khizroeva J, Khamani N. Thromboprophylaxis in pregnant women with thrombophilia and a history of thrombosis. Journal of Perinatal Medicine. 2018;46:893-899\u003c/li\u003e\n \u003cli\u003eBorella F, Marozio L, Bertschy G, Botta G, Bertero L, Cassoni P, et al. Placenta-mediated pregnancy complications in women with a history of late fetal loss and placental infarction without thrombophilia: Risk of recurrence and efficacy of pharmacological prophylactic interventions. A 10-year retrospective study. The Journal of Maternal-Fetal \u0026amp; Neonatal Medicine. 2023;36\u003c/li\u003e\n \u003cli\u003eSun B, Li L, Chen X, Sun Y. Effect of low-molecular-weight heparin in women undergoing frozen-thawed embryo transfer cycles: A retrospective cohort study. BMC Pregnancy and Childbirth. 2023;23\u003c/li\u003e\n \u003cli\u003eDonegan G, Noonan M, Bradshaw C,et al. Parents experiences of pregnancy following perinatal loss: An integrative review. Midwifery. 2023; DOI: 10.1016/j.midw.2023.103673.\u003c/li\u003e\n \u003cli\u003eKukulskien M , Emaitien N. Postnatal Depression and Post-Traumatic Stress Risk Following Miscarriage.International Journal of Environmental Research and Public Health. 2022;19(11):6515.\u003c/li\u003e\n \u003cli\u003eFarren J, Jalmbrant M, Falconieri, et al. Differences in post-traumatic stress, anxiety and depression following miscarriage or ectopic pregnancy between women and their partners: multicenter prospective cohort study. Ultrasound in obstetrics \u0026amp; gynecology: the official journal of the International Society of Ultrasound in Obstetrics and Gynecology. 2021; 57(1):141-148.\u003c/li\u003e\n \u003cli\u003eLiyan Wang, Lin Wang, Xia Yang,et al. Risk factors related to early pregnancy loss in fresh IVF/ICSI: An analysis of 954 embryo transfer cycles. Medicine (Baltimore). 2022;101(34):e30166.\u003c/li\u003e\n \u003cli\u003eMu F, Huo H, Wang C, et al. A new prognostic model for recurrent pregnancy loss: assessment of thyroid and thromboelastograph parameters. Frontiers in Endocrinology, 2024.1415786.\u003c/li\u003e\n \u003cli\u003eXu J, Yang Y, Guan G, Gao Y, Sun Q, Yuan G, et al. Prediction of unexplained recurrent miscarriages using thromboelastography. International Journal of Women\u0026apos;s Health. 2024;16:1573-1581.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"844\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"bottom\" style=\"width: 844px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1 Clinical characteristics and TEG of the participants\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eALL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003enon- Pregnancy loss\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ePregnancy loss\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eN=463\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eN=334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1 \u0026nbsp; N=129\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e32.00 [29.00,35.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e31.00 [29.00,34.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e32.00 [29.00,36.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; 0.047*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; 0.620 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eJunior high school or below\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e100 (21.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e68 (20.36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e32 (24.81%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eHigh school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e68 (14.69%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e48 (14.37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e20 (15.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eCollege\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e266 (57.45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e198 (59.28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e68 (52.71%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ePostgraduate or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e29 (6.26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e20 (5.99%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e9 (6.98%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eHusband\u0026apos;s age\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e33.00 [30.00,37.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e33.00 [30.00,36.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e34.00 [30.00,38.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; 0.157 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eMenarcheal age\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e13.00 [13.00,14.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e13.00 [13.00,14.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e13.00 [13.00,14.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; 0.467 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e22.06 [20.34,24.33]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e21.93 [20.19,24.41]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e22.66 [21.03,24.09]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eGravidity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.756\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e248（53.56%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e179(53.59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e69（53.49%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e115（24.84%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e87（26.05%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e28（21.71%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e54（11.67%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e38（11.38%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e16（12.40%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e26（5.62%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e17（5.09%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e9（6.98%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e20（4.32%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e13（3.90%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e7（5.43%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eParity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.021*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e386 (83.37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e288 (86.23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e98 (75.97%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e68 (14.69%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e40 (11.98%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e28 (21.71%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e9 (1.94%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e6 (1.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3 (2.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of abortions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.573\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e313 (67.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e224 (67.07%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e89 (68.99%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e97 (20.95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e73 (21.86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e24 (18.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e37 (7.99%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e24 (7.19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e13 (10.08%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e16 (3.46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e13 (3.89%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3 (2.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of Spontaneous abortion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e407 (87.90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e290 (86.83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e118 (90.77%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e46 (9.94%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e35 (10.48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e11 (8.46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e10 (2.16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e9 (2.69%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1 (0.77%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of children\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; 0.038 *\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e387 (83.59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e288 (86.23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e99 (76.74%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e70 (15.12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e42 (12.57%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e28 (21.71%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e6 (1.30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4 (1.20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2 (1.55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eEndometriosis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; 1.000 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e447 (96.54%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e322 (96.41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e125 (96.90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e16 (3.46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e12 (3.59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4 (3.10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003ePCOS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e393 (84.88%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e278 (83.23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e115 (89.15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e70 (15.12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e56 (16.77%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e14 (10.85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eTubal factor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.572\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e140 (30.24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e104 (31.14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e36 (27.91%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e323 (69.76%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e230 (68.86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e93 (72.09%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eOvulatory dysfunction\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; 0.001 *\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e435 (93.95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e322 (96.41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e113 (87.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e28 (6.05%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e12 (3.59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e16 (12.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnexplained infertility\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; 0.378 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e448 (96.76%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e325 (97.31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e123 (95.35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e15 (3.24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e9 (2.69%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e6 (4.65%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale factor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.524\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e160 (34.56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e112 (33.53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e48 (37.21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e303 (65.44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e222 (66.47%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e81 (62.79%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedical history \u0026amp;comorbidities\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eThyroid \u0026amp; endocrine disorders\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; 0.015 *\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e415 (89.63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e307 (91.92%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e108 (83.72%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e48 (10.37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e27 (8.08%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e21 (16.28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eRecurrent pregnancy loss\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e450 (97.19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e323 (96.71%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e127 (98.45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e13 (2.81%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e11 (3.29%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2 (1.55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eFertilization methods in ART\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eIVF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e400 (86.39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e287 (85.93%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e113 (87.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eICSI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e63 (13.61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e47 (14.07%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e16 (12.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eTEG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e61.10 [56.10,65.15]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e60.25 [55.90,64.68]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e5.40 [4.80,6.40]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; 0.004 *\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eMA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e5.60 [4.90,6.50]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e5.70 [5.00,6.50]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e62.90 [57.40,66.20]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; 0.012 *\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eLY30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.40 [1.20,1.80]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.50 [1.20,1.80]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.10 [0.10,0.10]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; 0.054 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.10 [0.10,0.50]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.10 [0.10,0.30]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.40 [1.20,1.70]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; 0.123 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eestimated percent lysis\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.70 [-0.45,1.60]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.50 [-0.58,1.50]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.10 [0.10,0.90]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; 0.002 *\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e69.50 [65.20,72.50]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e69.10 [64.82,72.30]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.00 [-0.20,2.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; 0.030 *\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eAngle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.10 [0.10,0.10]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.10 [0.10,0.10]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e69.90 [66.80,73.40]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; 0.094 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"bottom\"\u003e\n \u003cp\u003e*: A p-value \u0026lt;0.05 was considered statistically significant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"939\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"bottom\" style=\"width: 939px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2 Univariable logistic regression analysis of the influencing factors for pregnancy loss in IVF/ICSI cycles.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR(95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.02359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.056(1.008-1.106)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.022 #\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.11423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.895(0.716-1.122)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.332\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eHusband\u0026apos;s age\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.01835\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.028(0.992-1.066)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.129\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eMenarcheal age\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.0987\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.925(0.759-1.119)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.427\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eGravidity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.09129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.078(0.899-1.287)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.410\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eParity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.551\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.22511\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.735(1.111-2.697)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.014#\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of abortions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.13344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.965(0.737-1.246)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.790\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of Spontaneous abortion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-0.397\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.28643\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.673(0.365-1.135)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.166\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of children\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.528\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.695（1.066-2.679）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.024#\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.03126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.044(0.981-1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.170\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eFertilization methods in ART\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.31002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.865(0.459-1.557)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.639\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eRecurrent pregnancy loss\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-0.771\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.77581\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.462(0.071-1.753)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.320\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale factor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-0.161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.2159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.851(0.559-1.305)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.456\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnexplained infertility\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.566\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.53757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.762(0.58-4.988)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.292\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eOvulatory dysfunction\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.39723\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3.799(1.753-8.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.001#\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eThyroid \u0026amp; endocrine disorders\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.793\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.31173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.211(1.19-4.064)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.011#\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eTubal factor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.22911\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.168(0.75-1.845)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.498\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003ePCOS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-0.504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.31871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.604(0.313-1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.114\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eEndometriosis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-0.152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.58689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.859(0.237-2.518)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.795\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-0.273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.0939\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.761(0.63-0.911)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.004#\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eLY30\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.08835\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.878(0.709-1.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.141\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eK\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-0.309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.17706\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.734(0.509-1.022)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.080\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eestimated percent lysis\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.02312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.974(0.927-1.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.261\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eMA1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.01768\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.045(1.01-1.082)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.013#\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eCI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.06331\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.214(1.076-1.379)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.002#\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eAngle\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.01632\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.034(1.002-1.069)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.041#\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"bottom\"\u003e\n \u003cp\u003e#: A p-value \u0026lt;0.05 was considered statistically significant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"891\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"bottom\" style=\"width: 891px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 3 Multivariable logistic regression analysis of the influencing factors for pregnancy loss IVF/ICSI cycles.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003echaracteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR(95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eZ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.02533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.049 (0.998-1.102)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.889\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eParity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.479\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.23862\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.614 (1.004-2.571)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eThyroid and endocrine disorders\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.32618\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.202 (1.152-4.167)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.421\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eOvulatory dysfunction\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.40805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4.408 (1.992-10.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3.636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eCI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.06608\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.222 (1.077-1.397)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Coagulation index, Early pregnancy loss, Intracytoplasmic sperm injection, In vitro fertilization, Thromboelastography","lastPublishedDoi":"10.21203/rs.3.rs-8515894/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8515894/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cb\u003eIntroduction\u003c/b\u003e: Early pregnancy loss (EPL) is common among couples undergoing assisted reproductive technology(ART) treatment. This study aimed to investigate whether thromboelastography parameters on the day of embryo transfer, either alone or in combination with other clinical parameters, could predict subsequent EPL in in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI) cycles.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMethods\u003c/b\u003e: This study included 463 women who underwent IVF/ICSI cycles at the reproductive medicine center from May 2024 to May 2025. All these women underwent thromboelastography (TEG) on the day of embryo transfer, and their pregnancy outcomes were continuously followed up. For risk variables, we performed LASSO analysis. To analyze the risk factors associated with EPL, we employed univariate and multivariate logistic regression analyses. A nomogram was constructed for risk scoring and prediction. The area under the curve (AUC) was compared among different factors through the receiver operating characteristic (ROC) curve.\u003c/p\u003e \u003cp\u003e \u003cb\u003eResults\u003c/b\u003e: Among 463 women with clinical pregnancy, 129 (27.86%) experienced early pregnancy loss (\u0026lt;\u0026thinsp;12 weeks). There were significant differences in reaction time (R time), maximum amplitude (MA), expected percent lysis and coagulation index (CI) between women in the EPL group and those in the non-EPL group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Multivariate logistic regression analysis showed that parity (OR\u0026thinsp;=\u0026thinsp;1.614, 95%CI: 1.004\u0026ndash;2.571), thyroid and endocrine disorders(OR\u0026thinsp;=\u0026thinsp;2.202, 95%CI: 1.152\u0026ndash;4.167), ovulatory dysfunction(OR\u0026thinsp;=\u0026thinsp;4.408, 95%CI: 1.992\u0026ndash;10.01) and CI (OR\u0026thinsp;=\u0026thinsp;1.222, 95%CI: 1.077\u0026ndash;1.397) were influencing factors for EPL in IVF/ICSI cycles. ROC curve analysis demonstrated that the optimal cutoff value for CI in predicting EPL is 0.75. The AUC for all five factors combined was 0.672, with a sensitivity of 71.3% and a specificity of 43.4%, which was better than any single factor.\u003c/p\u003e \u003cp\u003e \u003cb\u003eConclusion\u003c/b\u003e: In IVF/ICSI cycles, a CI\u0026thinsp;\u0026gt;\u0026thinsp;0.75(on the day of embryo transfer)was significantly associated with increased risk of EPL.\u003c/p\u003e","manuscriptTitle":"The value of coagulation index in thromboelastograph for predicting early pregnancy loss in IVF/ICSI cycles","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-30 13:28:10","doi":"10.21203/rs.3.rs-8515894/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-03T17:17:46+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-02T07:33:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-30T14:36:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"311029909098587544288382215427335318894","date":"2026-01-30T12:12:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"271522904760230731491409711466002073634","date":"2026-01-29T14:58:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"10740003937267791652386822834449344184","date":"2026-01-29T12:54:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"117701424560537154075190580490508816403","date":"2026-01-29T09:51:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"32149160471514326225504470387365864121","date":"2026-01-28T12:12:52+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-28T11:18:27+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-28T11:15:15+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-19T11:28:49+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-16T08:45:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-01-16T08:36:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1df35356-05b2-4201-8200-7996dc89305b","owner":[],"postedDate":"January 30th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":61978648,"name":"Health sciences/Biomarkers"},{"id":61978649,"name":"Health sciences/Diseases"},{"id":61978650,"name":"Health sciences/Endocrinology"},{"id":61978651,"name":"Health sciences/Medical research"},{"id":61978652,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-03-30T16:17:36+00:00","versionOfRecord":{"articleIdentity":"rs-8515894","link":"https://doi.org/10.1038/s41598-026-43675-6","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-03-28 16:09:23","publishedOnDateReadable":"March 28th, 2026"},"versionCreatedAt":"2026-01-30 13:28:10","video":"","vorDoi":"10.1038/s41598-026-43675-6","vorDoiUrl":"https://doi.org/10.1038/s41598-026-43675-6","workflowStages":[]},"version":"v1","identity":"rs-8515894","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8515894","identity":"rs-8515894","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}